ECG-ArrestNetIHCA Prediction Platform
Department of Cardiology · Central South University
RESEARCH-USE ECG ANALYSIS PLATFORM

ECG-ArrestNet: In-Hospital Cardiac Arrest Prediction from 12-Lead ECG

Department of Cardiology, Second Xiangya Hospital, Central South University, Changsha, China
This research prototype demonstrates the inference workflow of ECG-ArrestNet, a deep learning model developed to estimate the risk of in-hospital cardiac arrest (IHCA) within 24 hours after index ECG acquisition from raw 12-lead ECG signals. Users may upload a recording or select a representative case, run the inference pipeline, and obtain a model-estimated IHCA probability with feature-level attribution. The model integrates a multi-scale 1D-CNN backbone, lead-aware cross-attention, a BiLSTM temporal encoder, and gated fusion of 52 ECG-derived features with deep-learning representations.
Uploaded ECG data are processed under strict privacy protection and never stored or shared.
01 — Prediction Workspace

Analyze a 12-lead ECG

Provide an ECG via a built-in sample case, file upload, or manual feature entry. ECG-ArrestNet extracts electrophysiologic features, runs the full inference pipeline, and returns a model-estimated IHCA probability with explainable contributions.

AECG Input
Upload a signal

Enter measured ECG features to run the prediction directly on the provided values.

12-LEAD ECG · 500 Hz · 10 sNormal Sinus Rhythm
Select a case and run the pipeline.
BInference & Results
Idle
✓
Stage 1
Multi-scale 1D-CNN
k=3/7/15/31 per lead
✓
Stage 2
Cross-Attention
8-head lead-aware
✓
Stage 3
BiLSTM Encoder
128 units, temporal
✓
Stage 4
Gated Fusion
52 ECG-derived features + deep ECG representation
Run a prediction to view the IHCA risk score, feature contributions, and lead attention.
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02 - About

Research prototype

ECG-ArrestNet is a research prototype developed to demonstrate an ECG-based deep learning workflow for estimating short-term IHCA risk after index ECG acquisition.

HHospital

Second Xiangya Hospital of Central South University — a tertiary referral center providing the case characteristics and ECG data for ECG-ArrestNet development and validation.

AArchitecture

Multi-scale 1D-CNN backbone, 8-head lead-aware cross-attention, BiLSTM temporal encoder, and gated fusion of 52 ECG-derived features with deep-learning representations.

PPrivacy

Uploaded ECG data are processed under strict data-protection safeguards and are never stored, shared, or used for any other purpose.

How to use this platform

Select a representative case or upload a 12-lead ECG file (XML or CSV, 500 Hz, 10 s). Adjust case characteristics (age, sex, care setting, ECG-to-event interval) if available, then run the prediction. The output includes a model-estimated IHCA probability, risk category, feature-level contributions, and a lead-attention heatmap.

03 — Clinical Notice

Important notice

Research prototype - not a standalone diagnostic device

This platform is a research prototype. It provides an IHCA risk probability to assist - not replace - clinical judgment. All predictions must be interpreted by qualified clinicians in conjunction with the patient's full clinical picture. In clinical use, predictions should be cross-validated against the hospital's electronic health record system. Uploaded signals are protected by strict data-security safeguards and are never transmitted or stored.

!Adjunctive use

ECG-ArrestNet is an adjunct to — not a substitute for — standard clinical assessment, vital-sign monitoring, and rapid-response protocols.

!Prevalence calibration

Risk probabilities should be interpreted in the context of local IHCA prevalence. PPV and NPV may differ from retrospective validation; recalibrate using Bayes' theorem for your setting.